FAQ
Publicis Sapient helps organizations apply generative AI and agentic AI to digital business transformation, customer experience, employee productivity, software development and data-driven decision-making. Its approach combines strategy, experience, engineering, data, governance and systems integration to move AI from experimentation to scalable business value.
What does Publicis Sapient help organizations do with AI?
Publicis Sapient helps organizations apply AI to real business transformation. Across these materials, that includes improving customer experience, employee workflows, software development, knowledge access, decision-making and operational efficiency. The emphasis is on solving business problems with the right mix of strategy, data, governance and technology rather than adopting AI for its own sake.
What is the difference between generative AI and agentic AI?
Generative AI creates new content, while agentic AI takes action to pursue goals. Publicis Sapient describes generative AI as producing text, images, audio or code based on patterns in training data, and agentic AI as systems that can plan, decide and execute multi-step processes with minimal human intervention. Agentic AI is presented as an application of multiple AI and systems capabilities, often building on generative AI rather than replacing it.
Why is generative AI being adopted faster than agentic AI?
Generative AI is being adopted faster because it is easier to deploy and scale. Publicis Sapient says generative AI delivers immediate value in areas like content creation, customer service and workflow support without always requiring deep system integration. Agentic AI can offer greater long-term impact, but it is harder to implement because it depends on connected systems, customized workflows, privacy controls and governance.
What business problems can Publicis Sapient help solve with generative AI?
Publicis Sapient helps organizations use generative AI to address inefficient processes, repetitive work, slow content creation, fragmented customer experiences and hard-to-access knowledge. The source materials highlight use cases such as conversational interfaces, summarization, personalization, workflow automation, software development support and decision support. The common goal is to simplify work, accelerate delivery and improve business outcomes.
How can generative AI improve customer experience?
Generative AI can improve customer experience by reducing friction, increasing personalization and making service more responsive. Publicis Sapient points to use cases such as conversational search, tailored recommendations, dynamic content generation, proactive self-service and faster access to relevant information. The materials also stress that backstage operational improvements can help employees deliver better customer experiences.
How does Publicis Sapient recommend companies approach AI for customer experience?
Publicis Sapient recommends starting with customer needs, not with the technology. The materials emphasize understanding the full customer journey, identifying pain points and prioritizing use cases tied to meaningful customer outcomes. Publicis Sapient also advises companies to focus on AI experiences that are useful, clear, reliable, impactful and ethical.
How can AI support employee productivity and creativity?
AI can support employee productivity and creativity by reducing manual work and helping teams focus on higher-value tasks. Publicis Sapient describes use cases such as ideation, first drafts, proofing, knowledge retrieval, response suggestions, workflow automation and decision support. The materials consistently position AI as a human-AI collaboration tool rather than a replacement for employee judgment.
How can AI help business leaders make better decisions?
AI can help business leaders make better decisions by analyzing information quickly and surfacing useful insights. Publicis Sapient cites examples such as using market trends, customer behavior, sales forecasting, employee sentiment and business scenario simulation to support planning and prioritization. In this role, AI is positioned as a strategic co-pilot that supports leadership rather than replacing it.
When should a company use generative AI instead of agentic AI?
A company should use generative AI when it needs faster implementation, lower deployment friction and support for content or knowledge-based work. Publicis Sapient points to use cases such as drafting content, summarizing information, answering questions, creating product descriptions and supporting customer service. Agentic AI is better suited to more complex, higher-value workflows that require real-time decisions and coordinated actions across systems.
When is a proprietary AI agent worth building?
A proprietary AI agent is worth building when the workflow is essential to the business, highly complex and time-sensitive. Publicis Sapient says custom agents make the most sense when the work depends on analyzing large amounts of data quickly and is core to business performance. For more standardized or non-core tasks, the materials suggest that third-party agent tools may be a faster and lower-cost option.
What is the biggest barrier to scaling agentic AI?
The biggest barrier to scaling agentic AI is systems integration. Publicis Sapient repeatedly explains that agentic AI is only useful when it can access the right inputs and act across the systems where work actually happens. If enterprise data, workflows and platforms are fragmented, agentic AI cannot operate reliably or autonomously at scale.
What are the most practical near-term use cases for agentic AI?
The most practical near-term use cases for agentic AI are repetitive, bounded workflows where speed and coordination matter. Publicis Sapient highlights customer service, scheduling, booking, documentation, supply chain response, software development and selected enterprise workflow orchestration as strong early candidates. These use cases are presented as good starting points because they can deliver value while still allowing human oversight.
What role does data play in AI success?
Data plays a central role in AI success. Publicis Sapient says AI outcomes depend heavily on data quality, relevance, completeness, accessibility and governance. The materials also note that poor, fragmented or biased data can undermine both generative AI and agentic AI, while strong data foundations improve reporting, decision-making and operational performance even before advanced AI is deployed.
What is AI-ready data?
AI-ready data is data that is clean, relevant, structured and well-governed for AI use. Publicis Sapient describes it as data that is accurate, accessible and organized in ways that support AI use cases, with controls for quality and management over time. The message across the materials is that better AI starts with better data readiness.
Why do many AI projects stall before launch?
Many AI projects stall before launch because experimentation alone is not enough. Publicis Sapient points to common barriers such as unclear business cases, weak data foundations, performance issues, regulatory hurdles and poor integration into existing workflows. The materials argue that moving from prototype to production requires strategy, governance, data readiness and operational alignment.
What risks should companies consider when adopting AI?
Companies should consider risks related to privacy, security, bias, misinformation, legal exposure, data leakage and overreliance on AI outputs. Publicis Sapient also highlights newer risks for agentic systems, such as data poisoning, reward hacking and unexpected infrastructure costs. Across the materials, the consistent recommendation is to pair AI adoption with clear guardrails, testing and human oversight.
How does Publicis Sapient recommend managing AI security, privacy and governance?
Publicis Sapient recommends building governance, privacy and risk management into AI initiatives from the start. The source materials describe measures such as ethical usage guidelines, avoiding confidential data where possible, anonymization, masking, pseudonymization, secure environments, access controls, zero-trust approaches and ongoing monitoring. The goal is to let organizations innovate while protecting sensitive information and maintaining trust.
What does responsible or ethical AI mean in these materials?
Responsible or ethical AI means using AI in ways that support fairness, privacy, transparency, accountability and business value. Publicis Sapient connects ethical AI to better product quality, stronger trust, lower legal and reputational risk and more sustainable operations. The materials also stress choosing the right tool for the job, including smaller models or non-AI solutions when those are a better fit.
Why does Publicis Sapient connect AI ethics with ESG?
Publicis Sapient connects AI ethics with ESG because responsible AI can support environmental, social and governance goals at the same time. The materials describe ethical AI as a way to reduce environmental impact through right-sized models, improve social outcomes by addressing bias and strengthen governance through privacy, transparency and accountability. Publicis Sapient also presents ethical AI as good business because it can improve trust, reduce costs and create better products.
How can AI improve software development and application modernization?
AI can improve software development across the full software development lifecycle, not just code generation. Publicis Sapient says AI can support planning, design, coding, testing, deployment, maintenance and modernization, with the biggest gains coming from workflow-wide adoption supported by enterprise context and skilled human review. The materials also describe AI-assisted modernization as a way to reduce costs, shorten timelines and lower defects in legacy transformation work.
What is Sapient Slingshot?
Sapient Slingshot is Publicis Sapient’s proprietary AI platform for accelerating software development and system integration. The source materials describe it as an ecosystem of AI agents used to automate activities such as code generation, testing, deployment and modernization. Publicis Sapient positions Sapient Slingshot as a fit for complex enterprise environments where generic tools may not provide the needed customization, security or integration.
What is PSChat?
PSChat is Publicis Sapient’s proprietary generative AI assistant for internal use. The materials describe it as a secure, organization-specific environment that helps employees ideate, automate work and access knowledge without relying only on public AI tools. Publicis Sapient presents PSChat as a way to improve productivity while helping protect company and client information.
What makes Publicis Sapient’s approach to AI different?
Publicis Sapient’s approach is positioned as business-led, end-to-end and multidisciplinary. Across these materials, the company emphasizes connecting strategy, product, experience, engineering, data, governance and change management rather than treating AI as an isolated pilot. The stated goal is to help organizations choose high-value use cases, build the right foundations and scale AI in ways that are secure, practical and measurable.